Ideas:
| Plant | Flowers | Date | lon | lat | ele | Month | Year | julian |
|---|---|---|---|---|---|---|---|---|
| Glossoloma oblongicalyx | 4 | 2015-10-19 | -78.59093 | 0.130838 | 2270 | October | 2015 | 292 |
| Gasteranthus quitensis | 2 | 2016-10-17 | -78.59770 | 0.120070 | 1940 | October | 2016 | 291 |
| Kohleria affinis | 1 | 2016-12-13 | -78.59534 | 0.126746 | 2110 | December | 2016 | 348 |
| Columnea ciliata | 3 | 2014-02-27 | -78.59934 | 0.116682 | 1960 | February | 2014 | 58 |
| Columnea medicinalis | 1 | 2014-04-23 | -78.59372 | 0.128700 | 2130 | April | 2014 | 113 |
| Drymonia teuscheri | 3 | 2016-07-28 | -78.59245 | 0.129393 | 2200 | July | 2016 | 210 |
NEEDS TO BE FIXED.
As range
Equal probability of flowering at anytime.
## sink("model/threshold_baseline.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- alpha[Plant[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<-alpha[PredPlant[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
##
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3164
## Total graph size: 15848
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 3
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19809
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 5
##
## #Strength of covariance decay
## lambda_cov = 2
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19809
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 3
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19965
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 5
##
## #Strength of covariance decay
## lambda_cov = 2
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19965
##
## Initializing model
## sink("model/threshold_attraction.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=inverse(vCov*gamma)
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 1
##
## #Strength of covariance decay
## lambda_cov = 3
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19965
##
## Initializing model
## sink("model/threshold_repulsion.jags")
## cat("
## model {
##
## for (x in 1:Nobs){
##
## #Observation of a flowering plant
## Y[x] ~ dbern(p[x])
## logit(p[x]) <- e[Plant[x],Site[x]]
##
## #Residuals
## discrepancy[x] <- abs(Y[x] - p[x])
##
## #Assess Model Fit
## Ynew[x] ~ dbern(p[x])
## discrepancy.new[x]<-abs(Ynew[x] - p[x])
## }
##
##
## #Sum discrepancy
## fit<-sum(discrepancy)/Nobs
## fitnew<-sum(discrepancy.new)/Nobs
##
## #Prediction
##
## for(x in 1:Npreds){
## #predict value
##
## #Observation - probability of flowering
## prediction[x] ~ dbern(p_new[x])
## logit(p_new[x])<- e[NewPlant[x],NewSite[x]]
##
## #predictive error
## pred_error[x] <- abs(Ypred[x] - prediction[x])
## }
##
## #Predictive Error
## fitpred<-sum(pred_error)/Npreds
##
## #########################
## #autocorrelation in error
## #########################
##
## #For each of observation
## for(y in 1:Sites){
## e[1:Plants,y] ~ dmnorm(zeros,tauC[,])
## }
##
## ##covariance among similiar species
## for(i in 1:Plants){
## for(j in 1:Plants){
## C[i,j] = exp(-lambda_cov * D[i,j])
## }
## }
##
## ## Covert variance to precision for each parameter, allow omega to shrink to identity matrix
## vCov = omega*C[,] + (1-omega) * I
## tauC=vCov*gamma
##
## #Priors
##
## #Species level priors
##
## for (j in 1:Plants){
##
## #Intercept flowering probability
## alpha[j] ~ dnorm(0,0.386)
##
## }
## #Autocorrelation priors
## gamma = 5
##
## #Strength of covariance decay
## lambda_cov = 2
## omega = 1
## }
## ",fill=TRUE)
##
## sink()
## Compiling model graph
## Resolving undeclared variables
## Allocating nodes
## Graph information:
## Observed stochastic nodes: 2548
## Unobserved stochastic nodes: 3170
## Total graph size: 19965
##
## Initializing model
# One example
Glossoloma purpureum
## # A tibble: 7 x 2
## Model p
## <chr> <dbl>
## 1 baseline 0.672
## 2 interaction_attraction 1
## 3 interaction_repulsion 1
## 4 phylogenetic_attraction 1
## 5 phylogenetic_repulsion 1
## 6 trait_attraction 1
## 7 trait_repulsion 1
Without baseline
## # A tibble: 6 x 2
## Model p
## <chr> <dbl>
## 1 interaction_attraction 1
## 2 interaction_repulsion 1
## 3 phylogenetic_attraction 1
## 4 phylogenetic_repulsion 1
## 5 trait_attraction 1
## 6 trait_repulsion 1
| Model | mean | lower | upper |
|---|---|---|---|
| interaction_repulsion | 0.4026453 | 0.3955924 | 0.4092325 |
| trait_repulsion | 0.4021917 | 0.3963085 | 0.4087652 |
| phylogenetic_repulsion | 0.3926034 | 0.3860373 | 0.3994986 |
| phylogenetic_attraction | 0.2144853 | 0.2077752 | 0.2206791 |
| trait_attraction | 0.2126021 | 0.2061591 | 0.2198152 |
| interaction_attraction | 0.2100346 | 0.2034172 | 0.2170060 |
| Model | mean | lower | upper |
|---|---|---|---|
| trait_repulsion | 0.4164950 | 0.3820598 | 0.4501661 |
| interaction_repulsion | 0.4164020 | 0.3837209 | 0.4485050 |
| phylogenetic_repulsion | 0.4058439 | 0.3753322 | 0.4368771 |
| baseline | 0.2439302 | 0.2192691 | 0.2691860 |
| phylogenetic_attraction | 0.2235814 | 0.1993355 | 0.2475083 |
| interaction_attraction | 0.2199767 | 0.1960133 | 0.2458472 |
| trait_attraction | 0.2199336 | 0.1943522 | 0.2441860 |
| Plant | Month | Observed | baseline | interaction_attraction | interaction_repulsion | phylogenetic_attraction | phylogenetic_repulsion | trait_attraction | trait_repulsion |
|---|---|---|---|---|---|---|---|---|---|
| Besleria solanoides | Apr | 13.3 | 17.3 | 15.3 | 45.8 | 19.1 | 38.5 | 16.7 | 42.9 |
| Besleria solanoides | Aug | 5.9 | 16.4 | 16.5 | 46.8 | 19.9 | 37.9 | 16.4 | 43.7 |
| Besleria solanoides | Dec | 12.5 | 15.6 | 16.2 | 46.7 | 21.5 | 38.9 | 18.3 | 45.5 |
| Besleria solanoides | Feb | 0.0 | 16.8 | 15.9 | 46.3 | 19.0 | 38.0 | 16.4 | 43.3 |
| Besleria solanoides | Jan | 0.0 | 16.6 | 16.2 | 48.2 | 20.6 | 40.1 | 17.8 | 44.8 |
| Besleria solanoides | Jul | 5.6 | 16.2 | 15.3 | 44.8 | 17.8 | 37.8 | 15.3 | 41.8 |
| Besleria solanoides | Jun | 5.0 | 15.7 | 15.6 | 45.4 | 18.4 | 37.9 | 15.5 | 44.3 |
| Besleria solanoides | Mar | 0.0 | 16.2 | 16.1 | 47.6 | 21.3 | 38.6 | 18.4 | 43.9 |
| Besleria solanoides | May | 0.0 | 16.6 | 16.1 | 46.7 | 20.1 | 39.0 | 16.5 | 43.9 |
| Besleria solanoides | Nov | 33.3 | 15.4 | 16.8 | 47.8 | 20.7 | 38.3 | 17.7 | 43.0 |
| Besleria solanoides | Oct | 55.6 | 16.2 | 17.2 | 47.5 | 20.7 | 38.2 | 17.2 | 43.5 |
| Besleria solanoides | Sep | 50.0 | 15.7 | 15.3 | 45.5 | 18.6 | 38.2 | 15.7 | 43.2 |
| Columnea ciliata | Apr | 46.7 | 16.7 | 17.8 | 48.1 | 17.2 | 47.9 | 18.1 | 47.3 |
| Columnea ciliata | Aug | 0.0 | 16.7 | 13.7 | 41.3 | 12.8 | 42.6 | 13.5 | 39.1 |
| Columnea ciliata | Dec | 0.0 | 16.8 | 15.5 | 43.5 | 13.4 | 43.9 | 15.4 | 42.1 |
| Columnea ciliata | Feb | 43.8 | 16.4 | 15.8 | 45.5 | 15.8 | 46.7 | 16.1 | 44.0 |
| Columnea ciliata | Jan | 25.0 | 16.4 | 17.5 | 48.5 | 16.7 | 49.4 | 18.0 | 47.3 |
| Columnea ciliata | Jul | 5.6 | 15.9 | 15.8 | 46.2 | 15.6 | 46.7 | 16.9 | 44.1 |
| Columnea ciliata | Jun | 10.0 | 16.5 | 15.8 | 45.4 | 15.3 | 45.7 | 15.5 | 43.1 |
| Columnea ciliata | Mar | 66.7 | 16.8 | 19.2 | 48.9 | 17.1 | 49.3 | 18.1 | 48.0 |
| Columnea ciliata | May | 5.9 | 15.7 | 16.5 | 46.2 | 15.5 | 46.4 | 16.3 | 45.0 |
| Columnea ciliata | Nov | 0.0 | 16.7 | 16.7 | 44.0 | 14.6 | 45.8 | 17.9 | 44.2 |
| Columnea ciliata | Oct | 0.0 | 16.5 | 16.0 | 42.5 | 14.0 | 44.5 | 15.2 | 42.7 |
| Columnea ciliata | Sep | 0.0 | 16.4 | 15.7 | 44.9 | 15.5 | 45.4 | 16.4 | 44.7 |
| Columnea kucyniakii | Apr | 6.7 | 9.1 | 10.1 | 39.1 | 10.5 | 38.4 | 10.1 | 40.7 |
| Columnea kucyniakii | Aug | 0.0 | 9.4 | 11.1 | 39.9 | 11.5 | 39.5 | 10.9 | 42.3 |
| Columnea kucyniakii | Dec | 12.5 | 9.3 | 12.0 | 39.9 | 11.4 | 41.4 | 12.0 | 41.0 |
| Columnea kucyniakii | Feb | 43.8 | 8.9 | 9.9 | 39.8 | 11.9 | 38.4 | 10.3 | 40.3 |
| Columnea kucyniakii | Jan | 25.0 | 9.0 | 11.3 | 40.1 | 12.4 | 40.1 | 10.9 | 41.7 |
| Columnea kucyniakii | Jul | 0.0 | 9.6 | 9.9 | 38.7 | 11.2 | 38.4 | 10.3 | 40.4 |
| Columnea kucyniakii | Jun | 0.0 | 9.4 | 11.4 | 39.1 | 11.6 | 37.8 | 10.3 | 40.8 |
| Columnea kucyniakii | Mar | 16.7 | 8.7 | 10.9 | 40.5 | 12.1 | 40.2 | 11.8 | 41.3 |
| Columnea kucyniakii | May | 0.0 | 9.3 | 11.7 | 40.4 | 12.4 | 40.9 | 11.8 | 40.8 |
| Columnea kucyniakii | Nov | 11.1 | 9.5 | 9.0 | 38.4 | 9.6 | 38.1 | 9.4 | 39.3 |
| Columnea kucyniakii | Oct | 0.0 | 9.2 | 10.1 | 39.3 | 11.5 | 38.7 | 10.7 | 41.2 |
| Columnea kucyniakii | Sep | 0.0 | 9.5 | 11.8 | 39.9 | 11.0 | 39.7 | 10.9 | 41.4 |
| Columnea medicinalis | Apr | 0.0 | 15.1 | 15.5 | 43.0 | 15.1 | 43.9 | 15.3 | 41.7 |
| Columnea medicinalis | Aug | 23.5 | 15.1 | 17.4 | 44.9 | 16.8 | 46.7 | 17.7 | 43.6 |
| Columnea medicinalis | Dec | 0.0 | 15.2 | 17.8 | 45.0 | 16.4 | 47.6 | 17.2 | 44.5 |
| Columnea medicinalis | Feb | 12.5 | 14.7 | 14.5 | 41.8 | 14.0 | 44.5 | 15.3 | 41.8 |
| Columnea medicinalis | Jan | 16.7 | 14.3 | 15.0 | 43.6 | 15.1 | 45.6 | 16.1 | 42.9 |
| Columnea medicinalis | Jul | 27.8 | 15.3 | 13.6 | 41.0 | 14.0 | 42.1 | 14.7 | 40.1 |
| Columnea medicinalis | Jun | 20.0 | 14.8 | 14.7 | 42.4 | 15.9 | 44.6 | 16.1 | 41.8 |
| Columnea medicinalis | Mar | 8.3 | 14.8 | 15.3 | 44.7 | 15.2 | 44.8 | 16.4 | 43.2 |
| Columnea medicinalis | May | 11.8 | 14.4 | 16.9 | 44.9 | 17.0 | 45.3 | 17.2 | 44.8 |
| Columnea medicinalis | Nov | 11.1 | 14.2 | 16.9 | 44.6 | 16.5 | 46.1 | 17.5 | 42.9 |
| Columnea medicinalis | Oct | 22.2 | 15.0 | 16.2 | 43.3 | 15.2 | 46.4 | 17.8 | 42.4 |
| Columnea medicinalis | Sep | 5.0 | 14.7 | 15.6 | 43.4 | 15.5 | 44.2 | 16.0 | 42.9 |
| Columnea picta | Apr | 13.3 | 13.8 | 15.7 | 44.1 | 15.2 | 46.0 | 15.2 | 46.8 |
| Columnea picta | Aug | 5.9 | 14.0 | 11.7 | 40.6 | 11.9 | 40.7 | 12.2 | 41.4 |
| Columnea picta | Dec | 12.5 | 14.3 | 14.5 | 43.6 | 14.2 | 43.0 | 14.8 | 45.1 |
| Columnea picta | Feb | 6.2 | 14.1 | 14.5 | 44.2 | 13.8 | 45.0 | 14.0 | 43.8 |
| Columnea picta | Jan | 16.7 | 14.0 | 15.2 | 43.3 | 14.7 | 44.5 | 15.0 | 44.7 |
| Columnea picta | Jul | 16.7 | 13.8 | 14.9 | 45.7 | 15.1 | 46.1 | 15.1 | 45.3 |
| Columnea picta | Jun | 25.0 | 15.2 | 14.0 | 43.8 | 14.4 | 44.0 | 14.1 | 44.1 |
| Columnea picta | Mar | 0.0 | 13.8 | 16.3 | 42.7 | 14.6 | 43.7 | 14.8 | 44.5 |
| Columnea picta | May | 35.3 | 14.2 | 17.0 | 47.5 | 17.7 | 47.9 | 17.1 | 47.9 |
| Columnea picta | Nov | 0.0 | 14.3 | 12.0 | 37.3 | 11.0 | 38.8 | 9.7 | 39.3 |
| Columnea picta | Oct | 0.0 | 14.0 | 10.9 | 38.1 | 10.1 | 38.1 | 9.9 | 39.1 |
| Columnea picta | Sep | 20.0 | 14.1 | 14.8 | 46.0 | 15.7 | 45.3 | 15.9 | 45.6 |
| Columnea strigosa | Apr | 0.0 | 12.7 | 14.0 | 38.4 | 12.3 | 41.6 | 14.2 | 38.5 |
| Columnea strigosa | Aug | 11.8 | 12.1 | 17.5 | 42.1 | 16.4 | 45.6 | 17.7 | 43.1 |
| Columnea strigosa | Dec | 0.0 | 12.3 | 14.9 | 38.8 | 13.2 | 42.4 | 15.5 | 38.0 |
| Columnea strigosa | Feb | 12.5 | 12.5 | 15.4 | 40.5 | 14.8 | 44.3 | 15.2 | 40.6 |
| Columnea strigosa | Jan | 8.3 | 11.9 | 11.8 | 37.0 | 11.8 | 40.0 | 11.7 | 36.2 |
| Columnea strigosa | Jul | 27.8 | 12.0 | 16.2 | 41.6 | 15.1 | 45.1 | 16.1 | 41.7 |
| Columnea strigosa | Jun | 40.0 | 12.1 | 15.9 | 40.6 | 14.8 | 44.5 | 15.9 | 40.5 |
| Columnea strigosa | Mar | 0.0 | 12.6 | 12.2 | 37.0 | 11.2 | 39.0 | 12.6 | 36.7 |
| Columnea strigosa | May | 23.5 | 13.2 | 12.1 | 36.5 | 11.5 | 39.3 | 13.2 | 37.1 |
| Columnea strigosa | Nov | 0.0 | 12.5 | 16.7 | 40.6 | 14.8 | 45.6 | 16.6 | 40.5 |
| Columnea strigosa | Oct | 0.0 | 12.3 | 17.8 | 40.5 | 14.7 | 45.5 | 16.8 | 42.5 |
| Columnea strigosa | Sep | 0.0 | 12.6 | 14.1 | 40.3 | 13.5 | 42.8 | 14.8 | 39.4 |
| Drymonia collegarum | Apr | 13.3 | 13.8 | 15.1 | 39.5 | 15.0 | 43.6 | 15.1 | 44.3 |
| Drymonia collegarum | Aug | 17.6 | 13.4 | 15.5 | 43.7 | 17.4 | 45.0 | 17.0 | 45.0 |
| Drymonia collegarum | Dec | 25.0 | 14.3 | 16.8 | 42.6 | 17.3 | 44.4 | 17.2 | 46.1 |
| Drymonia collegarum | Feb | 6.2 | 13.6 | 14.7 | 40.2 | 14.5 | 43.3 | 14.6 | 43.3 |
| Drymonia collegarum | Jan | 16.7 | 13.1 | 17.5 | 41.3 | 16.7 | 45.5 | 15.6 | 46.1 |
| Drymonia collegarum | Jul | 11.1 | 14.4 | 13.7 | 40.0 | 14.8 | 41.5 | 13.9 | 43.0 |
| Drymonia collegarum | Jun | 15.0 | 13.6 | 15.2 | 41.4 | 15.4 | 42.1 | 15.4 | 43.3 |
| Drymonia collegarum | Mar | 8.3 | 13.6 | 17.7 | 41.7 | 16.1 | 44.9 | 16.0 | 45.3 |
| Drymonia collegarum | May | 23.5 | 14.0 | 16.6 | 41.3 | 16.2 | 45.8 | 15.8 | 45.8 |
| Drymonia collegarum | Nov | 11.1 | 13.8 | 14.7 | 41.6 | 16.9 | 43.0 | 16.9 | 44.0 |
| Drymonia collegarum | Oct | 5.6 | 13.7 | 15.5 | 41.9 | 16.9 | 42.4 | 16.9 | 44.2 |
| Drymonia collegarum | Sep | 10.0 | 14.6 | 15.7 | 40.2 | 14.9 | 43.4 | 14.8 | 44.5 |
| Drymonia tenuis | Apr | 13.3 | 14.6 | 15.7 | 45.0 | 15.8 | 45.9 | 14.4 | 46.6 |
| Drymonia tenuis | Aug | 11.8 | 14.5 | 11.7 | 42.1 | 13.6 | 40.4 | 13.4 | 42.0 |
| Drymonia tenuis | Dec | 0.0 | 13.7 | 12.3 | 42.6 | 13.8 | 41.0 | 14.5 | 43.9 |
| Drymonia tenuis | Feb | 18.8 | 14.8 | 14.2 | 44.2 | 14.7 | 44.3 | 14.3 | 45.3 |
| Drymonia tenuis | Jan | 16.7 | 14.8 | 17.4 | 47.6 | 16.9 | 46.8 | 15.9 | 48.3 |
| Drymonia tenuis | Jul | 16.7 | 14.7 | 14.0 | 43.6 | 13.8 | 42.9 | 13.4 | 44.5 |
| Drymonia tenuis | Jun | 20.0 | 14.7 | 13.9 | 42.7 | 14.2 | 42.6 | 14.6 | 43.7 |
| Drymonia tenuis | Mar | 25.0 | 14.6 | 16.0 | 47.6 | 16.7 | 46.2 | 16.1 | 48.1 |
| Drymonia tenuis | May | 23.5 | 14.7 | 14.4 | 42.4 | 13.9 | 43.0 | 13.3 | 44.0 |
| Drymonia tenuis | Nov | 0.0 | 13.4 | 14.1 | 46.0 | 16.0 | 43.5 | 15.8 | 44.8 |
| Drymonia tenuis | Oct | 5.6 | 14.0 | 13.9 | 45.5 | 16.1 | 44.8 | 15.8 | 45.1 |
| Drymonia tenuis | Sep | 5.0 | 14.3 | 13.8 | 43.0 | 13.7 | 44.3 | 13.9 | 44.5 |
| Drymonia teuscheri | Apr | 26.7 | 17.9 | 16.0 | 45.6 | 15.4 | 46.6 | 15.6 | 46.3 |
| Drymonia teuscheri | Aug | 23.5 | 17.7 | 19.8 | 49.5 | 18.0 | 50.3 | 19.6 | 50.7 |
| Drymonia teuscheri | Dec | 0.0 | 16.8 | 18.6 | 48.2 | 18.1 | 47.7 | 17.7 | 47.5 |
| Drymonia teuscheri | Feb | 6.2 | 17.1 | 17.4 | 47.5 | 16.6 | 46.4 | 15.8 | 47.4 |
| Drymonia teuscheri | Jan | 16.7 | 17.3 | 15.2 | 43.3 | 14.2 | 43.6 | 14.3 | 43.3 |
| Drymonia teuscheri | Jul | 22.2 | 17.2 | 17.0 | 48.6 | 15.1 | 48.4 | 16.3 | 48.6 |
| Drymonia teuscheri | Jun | 15.0 | 17.0 | 16.8 | 48.7 | 16.4 | 48.4 | 17.0 | 48.9 |
| Drymonia teuscheri | Mar | 16.7 | 16.6 | 15.4 | 42.1 | 14.3 | 43.3 | 13.9 | 42.4 |
| Drymonia teuscheri | May | 29.4 | 17.6 | 16.6 | 44.0 | 15.4 | 45.1 | 16.5 | 45.1 |
| Drymonia teuscheri | Nov | 11.1 | 16.6 | 19.4 | 47.8 | 17.3 | 49.9 | 18.3 | 49.2 |
| Drymonia teuscheri | Oct | 11.1 | 16.8 | 19.5 | 49.2 | 18.1 | 49.0 | 18.0 | 49.3 |
| Drymonia teuscheri | Sep | 10.0 | 17.3 | 16.9 | 46.2 | 16.2 | 46.6 | 16.2 | 47.4 |
| Gasteranthus lateralis | Apr | 13.3 | 13.9 | 15.4 | 40.3 | 16.0 | 37.7 | 14.6 | 43.4 |
| Gasteranthus lateralis | Aug | 17.6 | 14.2 | 18.2 | 42.5 | 18.8 | 40.1 | 14.7 | 47.4 |
| Gasteranthus lateralis | Dec | 25.0 | 14.8 | 16.5 | 40.3 | 18.1 | 38.5 | 15.9 | 44.4 |
| Gasteranthus lateralis | Feb | 0.0 | 13.4 | 15.5 | 41.0 | 17.3 | 37.6 | 14.6 | 43.9 |
| Gasteranthus lateralis | Jan | 33.3 | 14.6 | 13.1 | 41.1 | 15.3 | 36.4 | 14.9 | 41.9 |
| Gasteranthus lateralis | Jul | 0.0 | 15.2 | 15.6 | 41.4 | 16.3 | 37.6 | 14.4 | 45.5 |
| Gasteranthus lateralis | Jun | 0.0 | 13.6 | 16.3 | 42.1 | 16.3 | 39.0 | 14.3 | 45.2 |
| Gasteranthus lateralis | Mar | 8.3 | 14.3 | 13.9 | 41.0 | 14.9 | 36.0 | 14.7 | 40.8 |
| Gasteranthus lateralis | May | 5.9 | 13.9 | 14.4 | 41.8 | 15.6 | 36.8 | 14.5 | 43.3 |
| Gasteranthus lateralis | Nov | 44.4 | 14.4 | 17.7 | 43.3 | 17.3 | 40.2 | 15.3 | 45.2 |
| Gasteranthus lateralis | Oct | 33.3 | 13.7 | 17.6 | 41.1 | 18.1 | 38.6 | 15.1 | 45.3 |
| Gasteranthus lateralis | Sep | 10.0 | 14.5 | 14.2 | 40.7 | 16.5 | 38.1 | 14.4 | 43.6 |
| Gasteranthus quitensis | Apr | 0.0 | 12.2 | 15.1 | 37.7 | 15.6 | 36.9 | 15.5 | 38.6 |
| Gasteranthus quitensis | Aug | 17.6 | 12.0 | 12.6 | 36.6 | 13.5 | 33.7 | 13.4 | 36.0 |
| Gasteranthus quitensis | Dec | 12.5 | 12.1 | 13.9 | 34.6 | 14.4 | 35.9 | 14.4 | 36.3 |
| Gasteranthus quitensis | Feb | 18.8 | 12.0 | 14.5 | 38.5 | 15.2 | 37.0 | 15.9 | 38.3 |
| Gasteranthus quitensis | Jan | 33.3 | 11.7 | 16.5 | 40.2 | 16.5 | 38.7 | 17.8 | 39.5 |
| Gasteranthus quitensis | Jul | 5.6 | 12.3 | 14.7 | 38.1 | 15.2 | 35.6 | 15.5 | 38.5 |
| Gasteranthus quitensis | Jun | 0.0 | 12.4 | 14.1 | 37.0 | 13.8 | 35.3 | 14.0 | 37.8 |
| Gasteranthus quitensis | Mar | 0.0 | 12.6 | 17.0 | 39.3 | 17.9 | 37.9 | 18.4 | 39.4 |
| Gasteranthus quitensis | May | 0.0 | 11.7 | 14.2 | 37.6 | 14.6 | 36.4 | 14.7 | 38.0 |
| Gasteranthus quitensis | Nov | 11.1 | 12.0 | 13.7 | 37.6 | 14.8 | 36.1 | 16.3 | 38.0 |
| Gasteranthus quitensis | Oct | 22.2 | 11.6 | 14.8 | 37.9 | 15.8 | 35.9 | 15.6 | 37.5 |
| Gasteranthus quitensis | Sep | 20.0 | 12.3 | 14.2 | 38.6 | 15.3 | 36.2 | 15.9 | 38.1 |
| Glossoloma oblongicalyx | Apr | 0.0 | 18.5 | 17.4 | 47.0 | 17.6 | 44.9 | 16.6 | 45.3 |
| Glossoloma oblongicalyx | Aug | 35.3 | 18.2 | 22.2 | 53.1 | 22.9 | 51.4 | 20.5 | 52.6 |
| Glossoloma oblongicalyx | Dec | 0.0 | 19.3 | 18.5 | 47.3 | 17.6 | 45.8 | 17.0 | 46.9 |
| Glossoloma oblongicalyx | Feb | 0.0 | 19.1 | 18.5 | 49.9 | 19.7 | 47.9 | 18.8 | 48.6 |
| Glossoloma oblongicalyx | Jan | 0.0 | 19.1 | 13.8 | 43.4 | 14.2 | 41.9 | 13.2 | 43.2 |
| Glossoloma oblongicalyx | Jul | 38.9 | 18.8 | 19.7 | 50.2 | 20.0 | 48.6 | 19.7 | 49.2 |
| Glossoloma oblongicalyx | Jun | 10.0 | 18.9 | 20.1 | 50.6 | 19.2 | 48.4 | 19.1 | 48.7 |
| Glossoloma oblongicalyx | Mar | 0.0 | 19.7 | 14.0 | 44.3 | 14.3 | 41.7 | 15.0 | 41.4 |
| Glossoloma oblongicalyx | May | 11.8 | 18.2 | 15.1 | 44.8 | 15.6 | 42.5 | 15.3 | 42.5 |
| Glossoloma oblongicalyx | Nov | 22.2 | 18.8 | 21.4 | 50.9 | 19.4 | 51.5 | 19.4 | 51.3 |
| Glossoloma oblongicalyx | Oct | 38.9 | 18.0 | 21.5 | 50.7 | 21.4 | 50.5 | 19.8 | 51.5 |
| Glossoloma oblongicalyx | Sep | 35.0 | 18.5 | 17.5 | 48.4 | 18.4 | 46.4 | 17.8 | 46.0 |
| Glossoloma purpureum | Apr | 6.7 | 14.2 | 15.4 | 45.3 | 16.5 | 43.6 | 15.9 | 43.4 |
| Glossoloma purpureum | Aug | 17.6 | 14.4 | 11.4 | 40.2 | 12.3 | 38.4 | 14.0 | 38.4 |
| Glossoloma purpureum | Dec | 12.5 | 14.1 | 13.5 | 42.4 | 14.1 | 42.3 | 15.8 | 41.9 |
| Glossoloma purpureum | Feb | 12.5 | 13.9 | 13.3 | 43.2 | 15.2 | 42.2 | 15.4 | 42.5 |
| Glossoloma purpureum | Jan | 16.7 | 13.9 | 14.7 | 43.6 | 14.7 | 42.9 | 14.8 | 42.3 |
| Glossoloma purpureum | Jul | 22.2 | 13.7 | 15.3 | 44.8 | 16.0 | 42.2 | 16.3 | 43.7 |
| Glossoloma purpureum | Jun | 10.0 | 13.7 | 14.2 | 44.1 | 15.6 | 41.5 | 15.6 | 42.9 |
| Glossoloma purpureum | Mar | 0.0 | 14.0 | 14.3 | 43.9 | 15.5 | 43.2 | 14.4 | 43.7 |
| Glossoloma purpureum | May | 5.9 | 14.0 | 17.2 | 47.2 | 18.5 | 46.3 | 17.9 | 46.6 |
| Glossoloma purpureum | Nov | 11.1 | 14.9 | 9.4 | 37.7 | 11.4 | 37.3 | 11.9 | 37.1 |
| Glossoloma purpureum | Oct | 11.1 | 12.7 | 9.3 | 37.7 | 10.5 | 35.8 | 11.2 | 36.0 |
| Glossoloma purpureum | Sep | 25.0 | 14.3 | 15.2 | 45.1 | 16.9 | 45.0 | 16.6 | 44.7 |
| Kohleria affinis | Apr | 46.7 | 16.0 | 16.5 | 45.8 | 18.8 | 37.3 | 15.5 | 45.7 |
| Kohleria affinis | Aug | 5.9 | 16.4 | 17.3 | 46.8 | 21.3 | 38.6 | 16.8 | 47.1 |
| Kohleria affinis | Dec | 12.5 | 15.2 | 16.9 | 46.3 | 21.5 | 39.1 | 17.6 | 47.1 |
| Kohleria affinis | Feb | 25.0 | 15.8 | 15.3 | 45.7 | 18.6 | 36.7 | 15.7 | 45.7 |
| Kohleria affinis | Jan | 8.3 | 15.3 | 16.5 | 47.7 | 20.6 | 37.3 | 16.3 | 46.0 |
| Kohleria affinis | Jul | 0.0 | 16.3 | 14.7 | 43.5 | 17.5 | 35.9 | 14.3 | 44.1 |
| Kohleria affinis | Jun | 15.0 | 15.3 | 15.7 | 45.6 | 18.4 | 37.0 | 15.8 | 45.1 |
| Kohleria affinis | Mar | 41.7 | 15.8 | 15.6 | 46.9 | 19.6 | 38.6 | 16.1 | 48.1 |
| Kohleria affinis | May | 29.4 | 15.6 | 17.0 | 47.5 | 20.5 | 38.7 | 16.0 | 47.1 |
| Kohleria affinis | Nov | 0.0 | 15.8 | 16.3 | 47.6 | 19.9 | 37.6 | 17.5 | 47.2 |
| Kohleria affinis | Oct | 0.0 | 16.0 | 16.9 | 47.8 | 20.6 | 37.2 | 16.5 | 47.3 |
| Kohleria affinis | Sep | 5.0 | 16.0 | 15.6 | 46.9 | 18.3 | 37.3 | 15.6 | 45.1 |
Grab an example Drymonia teuscheri v Drymonia collegarum
D["Drymonia teuscheri","Drymonia collegarum"]
## [1] 0.02974
Dtraits["Drymonia teuscheri","Drymonia collegarum"]
## [1] 0.04191869
Dint["Drymonia teuscheri","Drymonia collegarum"]
## [1] 0.3775183
## # A tibble: 6 x 4
## # Groups: Model [6]
## Model Var1 Var2 Correlation_E
## <chr> <fct> <fct> <dbl>
## 1 interaction_attraction Drymonia teuscheri Drymonia colle… -0.496
## 2 interaction_repulsion Drymonia teuscheri Drymonia colle… -0.0974
## 3 phylogenetic_attraction Drymonia teuscheri Drymonia colle… 0.750
## 4 phylogenetic_repulsion Drymonia teuscheri Drymonia colle… -0.635
## 5 trait_attraction Drymonia teuscheri Drymonia colle… 0.672
## 6 trait_repulsion Drymonia teuscheri Drymonia colle… -0.646
## # A tibble: 7 x 4
## # Groups: Model [7]
## Model Var1 Var2 Correlation_P
## <chr> <fct> <fct> <dbl>
## 1 baseline Drymonia teuscheri Drymonia colle… NA
## 2 interaction_attraction Drymonia teuscheri Drymonia colle… -0.394
## 3 interaction_repulsion Drymonia teuscheri Drymonia colle… -0.116
## 4 phylogenetic_attraction Drymonia teuscheri Drymonia colle… 0.471
## 5 phylogenetic_repulsion Drymonia teuscheri Drymonia colle… -0.620
## 6 trait_attraction Drymonia teuscheri Drymonia colle… 0.355
## 7 trait_repulsion Drymonia teuscheri Drymonia colle… -0.635
## # A tibble: 7 x 4
## # Groups: Model [7]
## Model Var1 Var2 Correlation_Ynew
## <chr> <fct> <fct> <dbl>
## 1 baseline Drymonia teuscheri Drymonia co… -0.171
## 2 interaction_attraction Drymonia teuscheri Drymonia co… -0.398
## 3 interaction_repulsion Drymonia teuscheri Drymonia co… -0.118
## 4 phylogenetic_attraction Drymonia teuscheri Drymonia co… 0.448
## 5 phylogenetic_repulsion Drymonia teuscheri Drymonia co… -0.639
## 6 trait_attraction Drymonia teuscheri Drymonia co… 0.372
## 7 trait_repulsion Drymonia teuscheri Drymonia co… -0.632